import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import sweetviz as sv
import statsmodels.api as sm
import matplotlib.pyplot as plt
from autoviz.AutoViz_Class import AutoViz_Class
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
from statsmodels.tsa.stattools import adfuller
from pmdarima import auto_arima
from prophet import Prophet
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
from statsmodels.tsa.arima.model import ARIMA
%matplotlib inline

def combineDieselColumns(csvPath):
    print("📂 Iniciando la combinación de las columnas de Diesel...")

    try:
        # Leer el archivo CSV en un DataFrame
        df = pd.read_csv(csvPath)
        print(f"✅ Archivo cargado: {csvPath}")
    except Exception as e:
        print(f"❌ Error al cargar {csvPath}: {e}")
        return None
    
    if 'Diesel alto azufre' not in df.columns or 'Diesel bajo azufre' not in df.columns:
        print("❌ Las columnas 'Diesel alto azufre' o 'Diesel bajo azufre' no existen en el archivo CSV.")
        return None

    # Crear la nueva columna 'Diesel'
    df['Diesel'] = df['Diesel alto azufre'].replace(0, pd.NA).combine_first(df['Diesel bajo azufre'].replace(0, pd.NA))
    
    print("🏁 Combinación de columnas completada.")
    return df

csvPath = 'Consumo/CONSUMO-2024-05.csv'
dataConsumo = combineDieselColumns(csvPath)

📂 Iniciando la combinación de las columnas de Diesel...
✅ Archivo cargado: Consumo/CONSUMO-2024-05.csv
🏁 Combinación de columnas completada.

dataConsumo = dataConsumo[['Fecha', 'Gasolina regular', 'Gasolina superior', 'Diesel', 'Gas licuado de petróleo']]
dataConsumo['Fecha'] = pd.to_datetime(dataConsumo['Fecha'])
dataConsumo.head()

#plt.figure(figsize=(12, 8))

#for i, column in enumerate(['Gasolina regular', 'Gasolina superior', 'Diesel', 'Gas licuado de petróleo'], 1):
#    plt.subplot(2, 2, i)
#    sns.histplot(dataConsumo[column], kde=True)
#    plt.title(f'Distribución de {column}')
#    plt.xlabel(column)

#plt.tight_layout()
#plt.show()

csvPath = 'Consumo/dataConsumo.csv'
dataConsumo.to_csv(csvPath, index=False)
AV = AutoViz_Class()
AV.AutoViz(csvPath)

Shape of your Data Set loaded: (293, 5)
#######################################################################################
######################## C L A S S I F Y I N G  V A R I A B L E S  ####################
#######################################################################################
Classifying variables in data set...
    Number of Numeric Columns =  4
    Number of Integer-Categorical Columns =  0
    Number of String-Categorical Columns =  0
    Number of Factor-Categorical Columns =  0
    Number of String-Boolean Columns =  0
    Number of Numeric-Boolean Columns =  0
    Number of Discrete String Columns =  0
    Number of NLP String Columns =  0
    Number of Date Time Columns =  0
    Number of ID Columns =  1
    Number of Columns to Delete =  0
    5 Predictors classified...
        1 variable(s) removed since they were ID or low-information variables
        List of variables removed: ['Fecha']
To fix these data quality issues in the dataset, import FixDQ from autoviz...
    All variables classified into correct types.

Number of All Scatter Plots = 10

All Plots done
Time to run AutoViz = 1 seconds 

 ###################### AUTO VISUALIZATION Completed ########################

#report = sv.analyze(dataConsumo)
#report.show_html('Consumo/EDAConsumo.html')

csvPath = 'Importacion/IMPORTACION-VOLUMEN-2024-05.csv'

dataImportacion = combineDieselColumns(csvPath)

📂 Iniciando la combinación de las columnas de Diesel...
✅ Archivo cargado: Importacion/IMPORTACION-VOLUMEN-2024-05.csv
🏁 Combinación de columnas completada.

dataImportacion = dataImportacion[['Fecha', 'Gasolina regular', 'Gasolina superior', 'Diesel', 'Gas licuado de petróleo']]
dataImportacion['Fecha'] = pd.to_datetime(dataImportacion['Fecha'])
dataImportacion.head()

csvPath = 'Importacion/dataImportacion.csv'
dataImportacion.to_csv(csvPath, index=False)
AV = AutoViz_Class()
AV.AutoViz(csvPath)

Shape of your Data Set loaded: (281, 5)
#######################################################################################
######################## C L A S S I F Y I N G  V A R I A B L E S  ####################
#######################################################################################
Classifying variables in data set...
    Number of Numeric Columns =  4
    Number of Integer-Categorical Columns =  0
    Number of String-Categorical Columns =  0
    Number of Factor-Categorical Columns =  0
    Number of String-Boolean Columns =  0
    Number of Numeric-Boolean Columns =  0
    Number of Discrete String Columns =  0
    Number of NLP String Columns =  0
    Number of Date Time Columns =  0
    Number of ID Columns =  1
    Number of Columns to Delete =  0
    5 Predictors classified...
        1 variable(s) removed since they were ID or low-information variables
        List of variables removed: ['Fecha']
To fix these data quality issues in the dataset, import FixDQ from autoviz...
    All variables classified into correct types.

Number of All Scatter Plots = 10

All Plots done
Time to run AutoViz = 1 seconds 

 ###################### AUTO VISUALIZATION Completed ########################

csvPath = 'PreciosPromedioNacionales/PreciosPromedioNacionales.csv'

dataPrecios = pd.read_csv(csvPath)
print(f"✅ Archivo cargado: {csvPath}")

✅ Archivo cargado: PreciosPromedioNacionales/PreciosPromedioNacionales.csv

dataPrecios = dataPrecios[['FECHA', 'Regular GTQ/GALON', 'Superior GTQ/GALON', 'Diesel GTQ/GALON', 'Glp Cilindro 25Lbs. GTQ/LB']]
dataPrecios['FECHA'] = pd.to_datetime(dataPrecios['FECHA'])
dataPrecios.head()

csvPath = 'PreciosPromedioNacionales/dataPrecios.csv'
dataPrecios.to_csv(csvPath, index=False)
AV = AutoViz_Class()
AV.AutoViz(csvPath)

Shape of your Data Set loaded: (1305, 5)
#######################################################################################
######################## C L A S S I F Y I N G  V A R I A B L E S  ####################
#######################################################################################
Classifying variables in data set...
    Number of Numeric Columns =  4
    Number of Integer-Categorical Columns =  0
    Number of String-Categorical Columns =  0
    Number of Factor-Categorical Columns =  0
    Number of String-Boolean Columns =  0
    Number of Numeric-Boolean Columns =  0
    Number of Discrete String Columns =  0
    Number of NLP String Columns =  0
    Number of Date Time Columns =  0
    Number of ID Columns =  1
    Number of Columns to Delete =  0
    5 Predictors classified...
        1 variable(s) removed since they were ID or low-information variables
        List of variables removed: ['FECHA']
To fix these data quality issues in the dataset, import FixDQ from autoviz...
    All variables classified into correct types.

Number of All Scatter Plots = 10

All Plots done
Time to run AutoViz = 1 seconds 

 ###################### AUTO VISUALIZATION Completed ########################

serieConsumoDiesel = dataConsumo[['Fecha', 'Diesel']]
serieImportacionRegular = dataImportacion[['Fecha', 'Gasolina regular']]
seriePreciosSuper = dataPrecios[['FECHA', 'Superior GTQ/GALON']]

serieConsumoDiesel.set_index('Fecha', inplace=True)
serieConsumoDiesel.sort_index(inplace=True)
serieImportacionRegular.set_index('Fecha', inplace=True)
serieImportacionRegular.sort_index(inplace=True)
seriePreciosSuper.set_index('FECHA', inplace=True)
seriePreciosSuper.sort_index(inplace=True)

def mostrarInfoSeriesTiempo(consumoDiesel, importacionRegular, preciosSuper):
    print("📊 Información de las series de tiempo:")

    print("\n🔵 Consumo Diesel:")
    print(f"   Inicio: {consumoDiesel.index.min()}")
    print(f"   Fin: {consumoDiesel.index.max()}")
    frecuenciaDiesel = consumoDiesel.index.freq or pd.infer_freq(consumoDiesel.index)
    print(f"   Frecuencia: {frecuenciaDiesel}")

    print("\n🟠 Importación Gasolina Regular:")
    print(f"   Inicio: {importacionRegular.index.min()}")
    print(f"   Fin: {importacionRegular.index.max()}")
    frecuenciaRegular = importacionRegular.index.freq or pd.infer_freq(importacionRegular.index)
    print(f"   Frecuencia: {frecuenciaRegular}")

    print("\n🟢 Precios Super:")
    print(f"   Inicio: {preciosSuper.index.min()}")
    print(f"   Fin: {preciosSuper.index.max()}")
    frecuenciaSuper = preciosSuper.index.freq or pd.infer_freq(preciosSuper.index)
    print(f"   Frecuencia: {frecuenciaSuper}")

# Supongamos que ya tienes las series de tiempo `serieConsumoDiesel`, `serieImportacionRegular` y `seriePreciosSuper`
mostrarInfoSeriesTiempo(serieConsumoDiesel, serieImportacionRegular, seriePreciosSuper)

📊 Información de las series de tiempo:

🔵 Consumo Diesel:
   Inicio: 2000-01-01 00:00:00
   Fin: 2024-05-01 00:00:00
   Frecuencia: MS

🟠 Importación Gasolina Regular:
   Inicio: 2001-01-01 00:00:00
   Fin: 2024-05-01 00:00:00
   Frecuencia: MS

🟢 Precios Super:
   Inicio: 2021-01-01 00:00:00
   Fin: 2024-07-28 00:00:00
   Frecuencia: D

def plotTimeSeries(consumoDiesel, importacionRegular, preciosSuper):
    print("📈 Iniciando la visualización de series de tiempo...")
    
    # Grafica Consumo Diesel
    plt.figure(figsize=(12, 6))
    print("🔵 Graficando Consumo Diesel...")
    plt.plot(consumoDiesel.index, consumoDiesel['Diesel'], label='Consumo Diesel', color='blue')
    plt.title('Consumo Diesel a lo largo del tiempo')
    plt.xlabel('Fecha')
    plt.ylabel('Consumo')
    plt.legend()
    plt.grid()
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.show()
    print("✅ Gráfica de Consumo Diesel completada.")

    # Grafica Importación Gasolina Regular
    plt.figure(figsize=(12, 6))
    print("🟠 Graficando Importación Gasolina Regular...")
    plt.plot(importacionRegular.index, importacionRegular['Gasolina regular'], label='Importación Gasolina Regular', color='orange')
    plt.title('Importación Gasolina Regular a lo largo del tiempo')
    plt.xlabel('Fecha')
    plt.ylabel('Importación')
    plt.legend()
    plt.grid()
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.show()
    print("✅ Gráfica de Importación Gasolina Regular completada.")

    # Grafica Precios Super
    plt.figure(figsize=(12, 6))
    print("🟢 Graficando Precios Super...")
    plt.plot(preciosSuper.index, preciosSuper['Superior GTQ/GALON'], label='Precios Super', color='green')
    plt.title('Precios Super a lo largo del tiempo')
    plt.xlabel('Fecha')
    plt.ylabel('Precio (GTQ/GALON)')
    plt.legend()
    plt.grid()
    plt.xticks(rotation=45)
    plt.tight_layout()
    plt.show()
    print("✅ Gráfica de Precios Super completada.")

plotTimeSeries(serieConsumoDiesel, serieImportacionRegular, seriePreciosSuper)

📈 Iniciando la visualización de series de tiempo...
🔵 Graficando Consumo Diesel...

✅ Gráfica de Consumo Diesel completada.
🟠 Graficando Importación Gasolina Regular...

✅ Gráfica de Importación Gasolina Regular completada.
🟢 Graficando Precios Super...

✅ Gráfica de Precios Super completada.

def descomponerSerieTiempo(serieTiempo, descripcion):
    print(f"📈 {descripcion}: Iniciando la descomposición de la serie de tiempo... ")
    
    # Descomposición de la serie
    descomposicion = sm.tsa.seasonal_decompose(serieTiempo, model='additive')
    
    # Graficar los componentes
    plt.figure(figsize=(12, 8))
    descomposicion.observed.plot(ax=plt.subplot(411), label='Observado', color='blue')
    plt.legend(loc='upper left')
    
    descomposicion.trend.plot(ax=plt.subplot(412), label='Tendencia', color='orange')
    plt.legend(loc='upper left')

    descomposicion.seasonal.plot(ax=plt.subplot(413), label='Estacionalidad', color='green')
    plt.legend(loc='upper left')

    descomposicion.resid.plot(ax=plt.subplot(414), label='Residuo', color='red')
    plt.legend(loc='upper left')

    plt.tight_layout()
    plt.show()
    
    print("✅ Descomposición completada.")

descomponerSerieTiempo(serieConsumoDiesel['Diesel'], 'Diesel')
descomponerSerieTiempo(serieImportacionRegular['Gasolina regular'], 'Regular')
descomponerSerieTiempo(seriePreciosSuper['Superior GTQ/GALON'], 'Superior')

📈 Diesel: Iniciando la descomposición de la serie de tiempo...

✅ Descomposición completada.
📈 Regular: Iniciando la descomposición de la serie de tiempo...

✅ Descomposición completada.
📈 Superior: Iniciando la descomposición de la serie de tiempo...

✅ Descomposición completada.

def graficarACF_PACF(serieTiempo, descripcion):
    print(f"📈 Gráfico de Autocorrelación y Autocorrelación Parcial para: {descripcion}")
    
    fig, axes = plt.subplots(1, 2, figsize=(16, 6))
    
    plot_acf(serieTiempo, lags=15, alpha=0.2, ax=axes[0])
    axes[0].set_title(f'Autocorrelación de {descripcion}')
    
    plot_pacf(serieTiempo, lags=15, alpha=0.2, ax=axes[1])
    axes[1].set_title(f'Autocorrelación Parcial de {descripcion}')
    
    plt.show()

# Ejemplos de uso:
graficarACF_PACF(serieConsumoDiesel['Diesel'], 'Diesel')
graficarACF_PACF(serieImportacionRegular['Gasolina regular'], 'Regular')
graficarACF_PACF(seriePreciosSuper['Superior GTQ/GALON'], 'Superior')

📈 Gráfico de Autocorrelación y Autocorrelación Parcial para: Diesel

📈 Gráfico de Autocorrelación y Autocorrelación Parcial para: Regular

📈 Gráfico de Autocorrelación y Autocorrelación Parcial para: Superior

def pruebaDickeyFuller(serieTiempo, descripcion):
    print(f"📊 Prueba de Dickey-Fuller Aumentada para: {descripcion}")
    resultado = adfuller(serieTiempo)
    print(f'Estadístico ADF: {resultado[0]}')
    print(f'p-valor: {resultado[1]}')
    print('Valores críticos:')
    for key, value in resultado[4].items():
        print(f'  {key}: {value}')
    print('-----------------------------------------------------')
    return resultado[1] 

print('-----------------------------------------------------')
pruebaDickeyFuller(serieConsumoDiesel['Diesel'], 'Diesel')
pruebaDickeyFuller(serieImportacionRegular['Gasolina regular'], 'Regular')
pruebaDickeyFuller(seriePreciosSuper['Superior GTQ/GALON'], 'Superior')

-----------------------------------------------------
📊 Prueba de Dickey-Fuller Aumentada para: Diesel
Estadístico ADF: 0.14523982117810716
p-valor: 0.9690175028779467
Valores críticos:
  1%: -3.453922368485787
  5%: -2.871918329081633
  10%: -2.5723001147959184
-----------------------------------------------------
📊 Prueba de Dickey-Fuller Aumentada para: Regular
Estadístico ADF: 0.8288289928204905
p-valor: 0.992087554110633
Valores críticos:
  1%: -3.4547128138328875
  5%: -2.8722649771800155
  10%: -2.5724850011573914
-----------------------------------------------------
📊 Prueba de Dickey-Fuller Aumentada para: Superior
Estadístico ADF: -2.9399220843939617
p-valor: 0.040898674582741115
Valores críticos:
  1%: -3.435401880796999
  5%: -2.863770985550096
  10%: -2.567957791647768
-----------------------------------------------------

0.040898674582741115

def detectar_valores_invalidos(serieTemporal, descripcion):
    serieTemporal = pd.to_numeric(serieTemporal, errors='coerce')  # Convertir a numérico y forzar NaN si hay errores
    n_nan = serieTemporal.isna().sum()
    n_no_positivos = (serieTemporal <= 0).sum()

    print(f"🔍 Detección de valores inválidos en la serie: {descripcion}")
    print(f"  Valores NaN: {n_nan}")
    print(f"  Valores no positivos: {n_no_positivos}")
    
    serieTemporal_sin_nan = serieTemporal.dropna()
    print(f"  Serie temporal sin valores NaN tiene {len(serieTemporal_sin_nan)} elementos.")
    
    return serieTemporal_sin_nan, n_nan, n_no_positivos

def varianza_estable(serieTemporal):
    n = len(serieTemporal)
    partes = np.array_split(serieTemporal, 3)
    varianzas = [np.var(parte) for parte in partes]
    return np.allclose(varianzas[0], varianzas[1], rtol=0.1) and np.allclose(varianzas[1], varianzas[2], rtol=0.1)

def aplicar_transformacion_logaritmica(serieTemporal):
    if (serieTemporal > 0).all():
        return np.log(serieTemporal)
    else:
        print("⚠️ La serie contiene valores no positivos, no se puede aplicar la transformación logarítmica.")
        return serieTemporal

def analizar_y_transformar_serie(serieTemporal, descripcion):
    print(f"🔍 Analizando la serie: {descripcion}")
    
    serieTemporal, n_nan, n_no_positivos = detectar_valores_invalidos(serieTemporal, descripcion)
    
    estacionaria = False
    intentos = 0
    max_intentos = 10

    serieTemporal = aplicar_transformacion_logaritmica(serieTemporal)
    
    while not estacionaria and intentos < max_intentos:
        intentos += 1
        p_valor = pruebaDickeyFuller(serieTemporal, descripcion)
        if p_valor < 0.05:
            if varianza_estable(serieTemporal):
                estacionaria = True
                print(f"✅ La serie {descripcion} es estacionaria en media y varianza.")
                break
            else:
                print(f"❌ La varianza no es estable en la serie {descripcion}.")
        else:
            print(f"❌ La serie {descripcion} no es estacionaria (p-valor: {p_valor}).")
        
        serieTemporal = serieTemporal.diff().dropna()
        print(f"🔄 Aplicando diferenciación a {descripcion}. Intento {intentos}.")
    
    if not estacionaria:
        print(f"⚠️ No se pudo hacer la serie {descripcion} estacionaria después de {max_intentos} intentos.")
        print("📈 Utilizando un modelo ARIMA para tratar con la no estacionariedad.")
        modelo_arima = ARIMA(serieTemporal, order=(1, 1, 1))
        modelo_arima_fit = modelo_arima.fit()
        print(modelo_arima_fit.summary())
    else:
        modelo_arima_fit = None
        
    return serieTemporal, modelo_arima_fit

# Asegúrate de que estas variables estén definidas correctamente
# Reemplaza con tus series temporales reales
serieTemporalD, modelo_arima_diesel = analizar_y_transformar_serie(serieConsumoDiesel['Diesel'], 'Diesel')
# print("--------------------------------------------------------------------------------------------------------")
serieTemporalR, modelo_arima_regular = analizar_y_transformar_serie(serieImportacionRegular['Gasolina regular'], 'Regular')
# print("--------------------------------------------------------------------------------------------------------")
serieTemporalS, modelo_arima_super = analizar_y_transformar_serie(seriePreciosSuper['Superior GTQ/GALON'], 'Superior')

🔍 Analizando la serie: Diesel
🔍 Detección de valores inválidos en la serie: Diesel
  Valores NaN: 0
  Valores no positivos: 0
  Serie temporal sin valores NaN tiene 293 elementos.
Prueba Dickey-Fuller para Diesel: p-valor = 0.901557753919378
❌ La serie Diesel no es estacionaria (p-valor: 0.901557753919378).
🔄 Aplicando diferenciación a Diesel. Intento 1.
Prueba Dickey-Fuller para Diesel: p-valor = 7.851113111171977e-10
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 2.
Prueba Dickey-Fuller para Diesel: p-valor = 3.1021697475811376e-15
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 3.
Prueba Dickey-Fuller para Diesel: p-valor = 7.339914914662219e-25
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 4.
Prueba Dickey-Fuller para Diesel: p-valor = 3.85887345410522e-27
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 5.
Prueba Dickey-Fuller para Diesel: p-valor = 3.960328765820069e-24
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 6.
Prueba Dickey-Fuller para Diesel: p-valor = 1.693758290598338e-19
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 7.
Prueba Dickey-Fuller para Diesel: p-valor = 9.999475164371196e-19
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 8.
Prueba Dickey-Fuller para Diesel: p-valor = 6.431107622936416e-23
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 9.
Prueba Dickey-Fuller para Diesel: p-valor = 2.6791261349428788e-28
❌ La varianza no es estable en la serie Diesel.
🔄 Aplicando diferenciación a Diesel. Intento 10.
⚠️ No se pudo hacer la serie Diesel estacionaria después de 10 intentos.
📈 Utilizando un modelo ARIMA para tratar con la no estacionariedad.
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                 Diesel   No. Observations:                  283
Model:                 ARIMA(1, 1, 1)   Log Likelihood               -1078.599
Date:                Sun, 04 Aug 2024   AIC                           2163.197
Time:                        14:50:24   BIC                           2174.123
Sample:                    11-01-2000   HQIC                          2167.578
                         - 05-01-2024                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.8892      0.027    -32.441      0.000      -0.943      -0.836
ma.L1         -0.9995      1.502     -0.665      0.506      -3.944       1.945
sigma2       119.3735    179.118      0.666      0.505    -231.692     470.439
===================================================================================
Ljung-Box (L1) (Q):                 162.95   Jarque-Bera (JB):                 0.09
Prob(Q):                              0.00   Prob(JB):                         0.96
Heteroskedasticity (H):               0.54   Skew:                             0.02
Prob(H) (two-sided):                  0.00   Kurtosis:                         3.08
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
🔍 Analizando la serie: Regular
🔍 Detección de valores inválidos en la serie: Regular
  Valores NaN: 0
  Valores no positivos: 0
  Serie temporal sin valores NaN tiene 281 elementos.
Prueba Dickey-Fuller para Regular: p-valor = 0.9547166884951503
❌ La serie Regular no es estacionaria (p-valor: 0.9547166884951503).
🔄 Aplicando diferenciación a Regular. Intento 1.
Prueba Dickey-Fuller para Regular: p-valor = 8.607670489056085e-12
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 2.
Prueba Dickey-Fuller para Regular: p-valor = 1.1514286931342563e-18
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 3.
Prueba Dickey-Fuller para Regular: p-valor = 1.4299582821292605e-21
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 4.
Prueba Dickey-Fuller para Regular: p-valor = 1.3446729756106482e-22
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 5.
Prueba Dickey-Fuller para Regular: p-valor = 2.3198979881378136e-21
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 6.
Prueba Dickey-Fuller para Regular: p-valor = 3.8522581992643975e-23
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 7.
Prueba Dickey-Fuller para Regular: p-valor = 4.3203576796413914e-27
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 8.
Prueba Dickey-Fuller para Regular: p-valor = 1.9830657561682155e-29
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 9.
Prueba Dickey-Fuller para Regular: p-valor = 7.512287476786966e-30
❌ La varianza no es estable en la serie Regular.
🔄 Aplicando diferenciación a Regular. Intento 10.
⚠️ No se pudo hacer la serie Regular estacionaria después de 10 intentos.
📈 Utilizando un modelo ARIMA para tratar con la no estacionariedad.
                               SARIMAX Results                                
==============================================================================
Dep. Variable:       Gasolina regular   No. Observations:                  271
Model:                 ARIMA(1, 1, 1)   Log Likelihood               -1459.374
Date:                Sun, 04 Aug 2024   AIC                           2924.747
Time:                        14:50:24   BIC                           2935.542
Sample:                    11-01-2001   HQIC                          2929.082
                         - 05-01-2024                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9238      0.022    -42.730      0.000      -0.966      -0.881
ma.L1         -0.9995      2.543     -0.393      0.694      -5.983       3.984
sigma2      2808.7948   7124.660      0.394      0.693   -1.12e+04    1.68e+04
===================================================================================
Ljung-Box (L1) (Q):                 171.55   Jarque-Bera (JB):                 6.09
Prob(Q):                              0.00   Prob(JB):                         0.05
Heteroskedasticity (H):               0.31   Skew:                            -0.02
Prob(H) (two-sided):                  0.00   Kurtosis:                         3.73
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
🔍 Analizando la serie: Superior
🔍 Detección de valores inválidos en la serie: Superior
  Valores NaN: 0
  Valores no positivos: 0
  Serie temporal sin valores NaN tiene 1305 elementos.
Prueba Dickey-Fuller para Superior: p-valor = 0.021117919557505475
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 1.
Prueba Dickey-Fuller para Superior: p-valor = 2.436091753573115e-16
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 2.
Prueba Dickey-Fuller para Superior: p-valor = 3.159810795761913e-22
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 3.
Prueba Dickey-Fuller para Superior: p-valor = 2.988167990607026e-27
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 4.
Prueba Dickey-Fuller para Superior: p-valor = 2.845364748853002e-30
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 5.
Prueba Dickey-Fuller para Superior: p-valor = 0.0
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 6.
Prueba Dickey-Fuller para Superior: p-valor = 0.0
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 7.
Prueba Dickey-Fuller para Superior: p-valor = 0.0
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 8.
Prueba Dickey-Fuller para Superior: p-valor = 0.0
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 9.
Prueba Dickey-Fuller para Superior: p-valor = 0.0
❌ La varianza no es estable en la serie Superior.
🔄 Aplicando diferenciación a Superior. Intento 10.
⚠️ No se pudo hacer la serie Superior estacionaria después de 10 intentos.
📈 Utilizando un modelo ARIMA para tratar con la no estacionariedad.
                               SARIMAX Results                                
==============================================================================
Dep. Variable:     Superior GTQ/GALON   No. Observations:                 1295
Model:                 ARIMA(1, 1, 1)   Log Likelihood               -1718.628
Date:                Sun, 04 Aug 2024   AIC                           3443.255
Time:                        14:50:25   BIC                           3458.752
Sample:                    01-11-2021   HQIC                          3449.071
                         - 07-28-2024                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.9007      0.006   -148.573      0.000      -0.913      -0.889
ma.L1         -0.9999      1.317     -0.759      0.448      -3.581       1.581
sigma2         0.8275      1.089      0.760      0.447      -1.307       2.962
===================================================================================
Ljung-Box (L1) (Q):                 755.72   Jarque-Bera (JB):              1613.69
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.76   Skew:                             0.00
Prob(H) (two-sided):                  0.01   Kurtosis:                         8.47
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).

serie_diesel = serieConsumoDiesel['Diesel']

# Ajustar el modelo ARIMA automáticamente
modelo_auto = auto_arima(serie_diesel,
                          seasonal=False,  # Si no es estacional
                          stepwise=True,   # Si utilizar pasos para encontrar el mejor modelo
                          trace=True)      # Mostrar el progreso

# Resumen del modelo
print(modelo_auto.summary())

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.29 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=7447.955, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=7440.658, Time=0.02 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=7440.979, Time=0.02 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=7446.248, Time=0.01 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=7442.638, Time=0.04 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=7442.627, Time=0.04 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=7444.623, Time=0.08 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=7439.163, Time=0.02 sec
 ARIMA(2,1,0)(0,0,0)[0]             : AIC=7441.126, Time=0.02 sec
 ARIMA(1,1,1)(0,0,0)[0]             : AIC=7441.116, Time=0.04 sec
 ARIMA(0,1,1)(0,0,0)[0]             : AIC=7439.504, Time=0.02 sec
 ARIMA(2,1,1)(0,0,0)[0]             : AIC=7443.087, Time=0.06 sec

Best model:  ARIMA(1,1,0)(0,0,0)[0]          
Total fit time: 0.677 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  293
Model:               SARIMAX(1, 1, 0)   Log Likelihood               -3717.581
Date:                Sun, 04 Aug 2024   AIC                           7439.163
Time:                        17:10:33   BIC                           7446.516
Sample:                    01-01-2000   HQIC                          7442.108
                         - 05-01-2024                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -0.1598      0.043     -3.719      0.000      -0.244      -0.076
sigma2      6.671e+09   4.16e-13    1.6e+22      0.000    6.67e+09    6.67e+09
===================================================================================
Ljung-Box (L1) (Q):                   0.35   Jarque-Bera (JB):                 1.44
Prob(Q):                              0.56   Prob(JB):                         0.49
Heteroskedasticity (H):               2.34   Skew:                            -0.14
Prob(H) (two-sided):                  0.00   Kurtosis:                         3.19
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number    inf. Standard errors may be unstable.

serie_regular = serieImportacionRegular['Gasolina regular']

# Ajustar el modelo ARIMA automáticamente
modelo_auto = auto_arima(serie_regular,
                          seasonal=False,  # Si no es estacional
                          stepwise=True,   # Si utilizar pasos para encontrar el mejor modelo
                          trace=True)      # Mostrar el progreso

# Resumen del modelo
print(modelo_auto.summary())

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=7193.005, Time=0.09 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=7412.355, Time=0.01 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=7264.099, Time=0.01 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=7230.431, Time=0.05 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=7410.471, Time=0.01 sec
 ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=7201.550, Time=0.07 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=7200.752, Time=0.06 sec
 ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=7193.700, Time=0.14 sec
 ARIMA(2,1,3)(0,0,0)[0] intercept   : AIC=7195.543, Time=0.13 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=7198.250, Time=0.04 sec
 ARIMA(1,1,3)(0,0,0)[0] intercept   : AIC=7200.602, Time=0.10 sec
 ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=7199.638, Time=0.06 sec
 ARIMA(3,1,3)(0,0,0)[0] intercept   : AIC=7189.517, Time=0.34 sec
 ARIMA(4,1,3)(0,0,0)[0] intercept   : AIC=inf, Time=0.35 sec
 ARIMA(3,1,4)(0,0,0)[0] intercept   : AIC=inf, Time=0.37 sec
 ARIMA(2,1,4)(0,0,0)[0] intercept   : AIC=inf, Time=0.40 sec
 ARIMA(4,1,2)(0,0,0)[0] intercept   : AIC=7195.774, Time=0.19 sec
 ARIMA(4,1,4)(0,0,0)[0] intercept   : AIC=inf, Time=0.45 sec
 ARIMA(3,1,3)(0,0,0)[0]             : AIC=7199.639, Time=0.16 sec

Best model:  ARIMA(3,1,3)(0,0,0)[0] intercept
Total fit time: 3.051 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  281
Model:               SARIMAX(3, 1, 3)   Log Likelihood               -3586.758
Date:                Sun, 04 Aug 2024   AIC                           7189.517
Time:                        17:10:38   BIC                           7218.595
Sample:                    01-01-2001   HQIC                          7201.180
                         - 05-01-2024                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
intercept    1.22e+04   2792.661      4.367      0.000    6721.770    1.77e+04
ar.L1         -1.4398      0.063    -22.896      0.000      -1.563      -1.317
ar.L2         -1.2383      0.086    -14.318      0.000      -1.408      -1.069
ar.L3         -0.2493      0.059     -4.218      0.000      -0.365      -0.133
ma.L1          0.3535      0.041      8.556      0.000       0.273       0.434
ma.L2         -0.0481      0.045     -1.062      0.288      -0.137       0.041
ma.L3         -0.8335      0.041    -20.336      0.000      -0.914      -0.753
sigma2       8.31e+09      0.001   1.53e+13      0.000    8.31e+09    8.31e+09
===================================================================================
Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):                37.62
Prob(Q):                              0.79   Prob(JB):                         0.00
Heteroskedasticity (H):               3.23   Skew:                             0.03
Prob(H) (two-sided):                  0.00   Kurtosis:                         4.80
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.1e+29. Standard errors may be unstable.

serie_super = seriePreciosSuper['Superior GTQ/GALON']

# Ajustar el modelo ARIMA automáticamente
modelo_auto_super = auto_arima(serie_super,
                          seasonal=False,  # Si no es estacional
                          stepwise=True,   # Si utilizar pasos para encontrar el mejor modelo
                          trace=True)      # Mostrar el progreso

# Resumen del modelo
print(modelo_auto.summary())

Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=773.104, Time=0.66 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=774.698, Time=0.05 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=776.697, Time=0.07 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=776.697, Time=0.05 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=773.556, Time=0.03 sec
 ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=779.616, Time=0.11 sec
 ARIMA(2,1,1)(0,0,0)[0] intercept   : AIC=770.964, Time=0.69 sec
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=778.676, Time=0.12 sec
 ARIMA(2,1,0)(0,0,0)[0] intercept   : AIC=777.650, Time=0.05 sec
 ARIMA(3,1,1)(0,0,0)[0] intercept   : AIC=781.620, Time=0.09 sec
 ARIMA(3,1,0)(0,0,0)[0] intercept   : AIC=779.621, Time=0.08 sec
 ARIMA(3,1,2)(0,0,0)[0] intercept   : AIC=771.049, Time=0.99 sec
 ARIMA(2,1,1)(0,0,0)[0]             : AIC=769.409, Time=0.17 sec
 ARIMA(1,1,1)(0,0,0)[0]             : AIC=769.647, Time=0.21 sec
 ARIMA(2,1,0)(0,0,0)[0]             : AIC=776.461, Time=0.03 sec
 ARIMA(3,1,1)(0,0,0)[0]             : AIC=780.423, Time=0.06 sec
 ARIMA(2,1,2)(0,0,0)[0]             : AIC=771.199, Time=0.25 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=775.556, Time=0.02 sec
 ARIMA(1,1,2)(0,0,0)[0]             : AIC=778.410, Time=0.04 sec
 ARIMA(3,1,0)(0,0,0)[0]             : AIC=778.423, Time=0.10 sec
 ARIMA(3,1,2)(0,0,0)[0]             : AIC=768.220, Time=0.37 sec
 ARIMA(4,1,2)(0,0,0)[0]             : AIC=770.135, Time=0.55 sec
 ARIMA(3,1,3)(0,0,0)[0]             : AIC=760.596, Time=0.73 sec
 ARIMA(2,1,3)(0,0,0)[0]             : AIC=768.273, Time=0.54 sec
 ARIMA(4,1,3)(0,0,0)[0]             : AIC=753.584, Time=0.58 sec
 ARIMA(5,1,3)(0,0,0)[0]             : AIC=754.952, Time=0.76 sec
 ARIMA(4,1,4)(0,0,0)[0]             : AIC=755.142, Time=0.88 sec
 ARIMA(3,1,4)(0,0,0)[0]             : AIC=753.109, Time=0.72 sec
 ARIMA(2,1,4)(0,0,0)[0]             : AIC=770.272, Time=0.56 sec
 ARIMA(3,1,5)(0,0,0)[0]             : AIC=752.713, Time=0.76 sec
 ARIMA(2,1,5)(0,0,0)[0]             : AIC=750.713, Time=0.48 sec
 ARIMA(1,1,5)(0,0,0)[0]             : AIC=783.759, Time=0.26 sec
 ARIMA(1,1,4)(0,0,0)[0]             : AIC=782.318, Time=0.12 sec
 ARIMA(2,1,5)(0,0,0)[0] intercept   : AIC=751.866, Time=1.66 sec

Best model:  ARIMA(2,1,5)(0,0,0)[0]          
Total fit time: 12.850 seconds
                               SARIMAX Results                                
==============================================================================
Dep. Variable:                      y   No. Observations:                  281
Model:               SARIMAX(3, 1, 3)   Log Likelihood               -3586.758
Date:                Sun, 04 Aug 2024   AIC                           7189.517
Time:                        17:10:53   BIC                           7218.595
Sample:                    01-01-2001   HQIC                          7201.180
                         - 05-01-2024                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
intercept    1.22e+04   2792.661      4.367      0.000    6721.770    1.77e+04
ar.L1         -1.4398      0.063    -22.896      0.000      -1.563      -1.317
ar.L2         -1.2383      0.086    -14.318      0.000      -1.408      -1.069
ar.L3         -0.2493      0.059     -4.218      0.000      -0.365      -0.133
ma.L1          0.3535      0.041      8.556      0.000       0.273       0.434
ma.L2         -0.0481      0.045     -1.062      0.288      -0.137       0.041
ma.L3         -0.8335      0.041    -20.336      0.000      -0.914      -0.753
sigma2       8.31e+09      0.001   1.53e+13      0.000    8.31e+09    8.31e+09
===================================================================================
Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):                37.62
Prob(Q):                              0.79   Prob(JB):                         0.00
Heteroskedasticity (H):               3.23   Skew:                             0.03
Prob(H) (two-sided):                  0.00   Kurtosis:                         4.80
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.1e+29. Standard errors may be unstable.

def calcular_aic_bic(y_true, y_pred, num_params):
    # Calcular log-verosimilitud
    residuals = y_true - y_pred
    n = len(y_true)
    log_likelihood = -0.5 * (n * np.log(np.sum(residuals**2) / n))
    
    # Calcular AIC y BIC
    aic = 2 * num_params - 2 * log_likelihood
    bic = np.log(n) * num_params - 2 * log_likelihood
    
    return aic, bic

def entrenarProphet(serieTiempo, nombre_variable):
    # Preparar los datos
    df_prophet = serieTiempo.reset_index()
    df_prophet.columns = ['ds', 'y']

    # Crear y ajustar el modelo Prophet
    modelo = Prophet()
    modelo.fit(df_prophet)

    # Hacer predicciones
    futuro = modelo.make_future_dataframe(periods=12, freq='M')  # Cambia el número y la frecuencia según tus necesidades
    pronostico = modelo.predict(futuro)

    # Graficar resultados
    modelo.plot(pronostico)
    plt.title(f'Pronóstico de {nombre_variable} usando Prophet')
    plt.show()

    # Calcular AIC y BIC
    y_true = df_prophet['y'].values
    y_pred = pronostico['yhat'][:len(df_prophet)].values
    num_params = len(modelo.params)  # Número de parámetros del modelo
    
    aic, bic = calcular_aic_bic(y_true, y_pred, num_params)
    
    print(f"AIC para {nombre_variable}: {aic}")
    print(f"BIC para {nombre_variable}: {bic}")

# Entrenar el modelo para el Diesel
entrenarProphet(serieConsumoDiesel[['Diesel']], 'Diesel')
entrenarProphet(serieImportacionRegular[['Gasolina regular']], 'Regular')
entrenarProphet(seriePreciosSuper[['Superior GTQ/GALON']], 'Superior')

17:20:53 - cmdstanpy - INFO - Chain [1] start processing
17:20:53 - cmdstanpy - INFO - Chain [1] done processing

17:20:53 - cmdstanpy - INFO - Chain [1] start processing
17:20:53 - cmdstanpy - INFO - Chain [1] done processing

AIC para Diesel: 6480.70112158008
BIC para Diesel: 6506.4623298432

17:20:54 - cmdstanpy - INFO - Chain [1] start processing

AIC para Regular: 6387.850788531148
BIC para Regular: 6413.319271216485

17:20:54 - cmdstanpy - INFO - Chain [1] done processing

AIC para Superior: -279.6520722629985
BIC para Superior: -243.43436402470093

from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error


ultimos_3_anos = serieTemporalD['2022-01-01':'2024-12-31']

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(ultimos_3_anos)
train_size = int(n * 0.7)
train, test = ultimos_3_anos[:train_size], ultimos_3_anos[train_size:]

pred = modelo_arima_diesel.get_prediction(start=test.index[0], end=test.index[-1])

pred_ci = pred.conf_int()
ax = serieTemporalD['2022':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)

# Evaluar el modelo
rmse = np.sqrt(mean_squared_error(test, pred.predicted_mean))
mae = mean_absolute_error(test, pred.predicted_mean)
mape = mean_absolute_percentage_error(test, pred.predicted_mean)

print(f'RMSE: {rmse:.2f}')
print(f'MAE: {mae:.2f}')
print(f'MAPE: {mape * 100:.2f}%')  
plt.legend()
plt.title(f'Predicción últimos 3 años Diesel')
plt.show()

RMSE: 9.34
MAE: 8.38
MAPE: 47.00%

ultimos_3_anos = serieTemporalR['2022-01-01':'2024-12-31']

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(ultimos_3_anos)
train_size = int(n * 0.7)
train, test = ultimos_3_anos[:train_size], ultimos_3_anos[train_size:]

pred = modelo_arima_regular.get_prediction(start=test.index[0], end=test.index[-1])

pred_ci = pred.conf_int()
ax = serieTemporalR['2022':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)

# Evaluar el modelo
rmse = np.sqrt(mean_squared_error(test, pred.predicted_mean))
mae = mean_absolute_error(test, pred.predicted_mean)
mape = mean_absolute_percentage_error(test, pred.predicted_mean)

print(f'RMSE: {rmse:.2f}')
print(f'MAE: {mae:.2f}')
print(f'MAPE: {mape * 100:.2f}%')  

plt.legend()
plt.title(f'Predicción últimos 3 años Regular')
plt.show()

pred

RMSE: 17.56
MAE: 13.31
MAPE: 136.39%

<statsmodels.tsa.statespace.mlemodel.PredictionResultsWrapper at 0x2dc4c777f90>

ultimos_3_anos = serieTemporalS['2022-01-01':'2024-12-31']

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(ultimos_3_anos)
train_size = int(n * 0.7)
train, test = ultimos_3_anos[:train_size], ultimos_3_anos[train_size:]

pred = modelo_arima_super.get_prediction(start=test.index[0], end=test.index[-1])

pred_ci = pred.conf_int()
ax = serieTemporalS['2022':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)

# Evaluar el modelo
rmse = np.sqrt(mean_squared_error(test, pred.predicted_mean))
mae = mean_absolute_error(test, pred.predicted_mean)
mape = mean_absolute_percentage_error(test, pred.predicted_mean)

print(f'RMSE: {rmse:.2f}')
print(f'MAE: {mae:.2f}')
print(f'MAPE: {mape * 100:.2f}%')  

plt.legend()
plt.title(f'Predicción últimos 3 años Superior')
plt.show()

pred

RMSE: 0.77
MAE: 0.52
MAPE: 7882774729377.25%

<statsmodels.tsa.statespace.mlemodel.PredictionResultsWrapper at 0x2dc68fb6b50>

actual_year = serieTemporalD['2024-01-01':'2024-12-31']

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(actual_year)
train_size = int(n * 0.7)
train, test = actual_year[:train_size], actual_year[train_size:]

pred = modelo_arima_diesel.get_prediction(start=test.index[0], end='2024-12-31')

pred_ci = pred.conf_int()
ax = serieTemporalD['2024':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)





plt.legend()
plt.title(f'Predicción 2024 Diesel')
plt.show()

# Alinear las predicciones y los valores reales para asegurarse de que tienen la misma longitud
predictions = pred.predicted_mean.loc[test.index]
true_values = test

# Calcular intervalo de confianza
pred_ci = pred.conf_int().loc[test.index]

# Calcular errores
rmse = np.sqrt(mean_squared_error(true_values, predictions))
mae = mean_absolute_error(true_values, predictions)
mape = np.mean(np.abs((true_values - predictions) / true_values)) * 100

print(f'RMSE: {rmse}')
print(f'MAE: {mae}')
print(f'MAPE: {mape:.2f}%')

RMSE: 4.790903186877741
MAE: 4.561657135595812
MAPE: 22.95%

actual_year = serieTemporalR['2024-01-01':'2024-12-31']

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(actual_year)
train_size = int(n * 0.7)
train, test = actual_year[:train_size], actual_year[train_size:]

pred = modelo_arima_regular.get_prediction(start=test.index[0], end='2024-12-31')

pred_ci = pred.conf_int()
ax = serieTemporalR['2024':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)

plt.title(f'Predicción 2024 Regular')
plt.legend()
plt.show()


# Alinear las predicciones y los valores reales para asegurarse de que tienen la misma longitud
predictions = pred.predicted_mean.loc[test.index]
true_values = test

# Calcular intervalo de confianza
pred_ci = pred.conf_int().loc[test.index]

# Calcular errores
rmse = np.sqrt(mean_squared_error(true_values, predictions))
mae = mean_absolute_error(true_values, predictions)
mape = np.mean(np.abs((true_values - predictions) / true_values)) * 100

print(f'RMSE: {rmse}')
print(f'MAE: {mae}')
print(f'MAPE: {mape:.2f}%')

RMSE: 31.710895539342097
MAE: 31.408330118821873
MAPE: 321.42%

actual_year = serieTemporalS['2024-01-01':'2024-12-31']

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(actual_year)
train_size = int(n * 0.7)
train, test = actual_year[:train_size], actual_year[train_size:]

pred = modelo_arima_super.get_prediction(start=test.index[0], end='2024-12-31')

pred_ci = pred.conf_int()
ax = serieTemporalS['2024':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)

plt.legend()
plt.title(f'Predicción 2024 Superior')
plt.show()


# Alinear las predicciones y los valores reales para asegurarse de que tienen la misma longitud
predictions = pred.predicted_mean.loc[test.index]
true_values = test

# Calcular intervalo de confianza
pred_ci = pred.conf_int().loc[test.index]

# Calcular errores
rmse = np.sqrt(mean_squared_error(true_values, predictions))
mae = mean_absolute_error(true_values, predictions)
mape = np.mean(np.abs((true_values - predictions) / true_values)) * 100

print(f'RMSE: {rmse}')
print(f'MAE: {mae}')
print(f'MAPE: {mape:.2f}%')

RMSE: 0.32406539665899864
MAE: 0.23132556421351486
MAPE: inf%

actual_year = serieTemporalD['2020-01-01':]

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(actual_year)
train_size = int(n * 0.7)
train, test = actual_year[:train_size], actual_year[train_size:]

pred = modelo_arima_diesel.get_prediction(start=test.index[0], end='2024-12-31')

pred_ci = pred.conf_int()
ax = serieTemporalD['2020':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)


plt.legend()
plt.title(f'Comportamiendo en 2020')
plt.show()

# Alinear las predicciones y los valores reales para asegurarse de que tienen la misma longitud
predictions = pred.predicted_mean.loc[test.index]
true_values = test

# Calcular intervalo de confianza
pred_ci = pred.conf_int().loc[test.index]

# Calcular errores
rmse = np.sqrt(mean_squared_error(true_values, predictions))
mae = mean_absolute_error(true_values, predictions)
mape = np.mean(np.abs((true_values - predictions) / true_values)) * 100

print(f'RMSE: {rmse}')
print(f'MAE: {mae}')
print(f'MAPE: {mape:.2f}%')

RMSE: 10.78909174355549
MAE: 9.528774374289872
MAPE: 36.71%

actual_year = serieTemporalR['2020-01-01':]

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(actual_year)
train_size = int(n * 0.7)
train, test = actual_year[:train_size], actual_year[train_size:]

pred = modelo_arima_regular.get_prediction(start=test.index[0], end='2024-12-31')

pred_ci = pred.conf_int()
ax = serieTemporalR['2020':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)

plt.title(f'Comportamiento en 2020')
plt.legend()
plt.show()


# Alinear las predicciones y los valores reales para asegurarse de que tienen la misma longitud
predictions = pred.predicted_mean.loc[test.index]
true_values = test

# Calcular intervalo de confianza
pred_ci = pred.conf_int().loc[test.index]

# Calcular errores
rmse = np.sqrt(mean_squared_error(true_values, predictions))
mae = mean_absolute_error(true_values, predictions)
mape = np.mean(np.abs((true_values - predictions) / true_values)) * 100

print(f'RMSE: {rmse}')
print(f'MAE: {mae}')
print(f'MAPE: {mape:.2f}%')

RMSE: 18.11992975727239
MAE: 13.964687409995861
MAPE: 90.00%

actual_year = serieTemporalS['2021-01-01':]

# Dividir los datos en 70% entrenamiento y 30% prueba
n = len(actual_year)
train_size = int(n * 0.7)
train, test = actual_year[:train_size], actual_year[train_size:]

pred = modelo_arima_super.get_prediction(start=test.index[0], end='2024-12-31')

pred_ci = pred.conf_int()
ax = serieTemporalS['2021':].plot(label='observed')
pred.predicted_mean.plot(ax=ax, label='One-step ahead Forecast', alpha=.7, figsize=(14, 4))
ax.fill_between( pred_ci.iloc[:,0],
                pred_ci.iloc[:,1], color='k', alpha=.2)

plt.legend()
plt.title(f'Predicción 2024 Superior')
plt.show()


# Alinear las predicciones y los valores reales para asegurarse de que tienen la misma longitud
predictions = pred.predicted_mean.loc[test.index]
true_values = test

# Calcular intervalo de confianza
pred_ci = pred.conf_int().loc[test.index]

# Calcular errores
rmse = np.sqrt(mean_squared_error(true_values, predictions))
mae = mean_absolute_error(true_values, predictions)
mape = np.mean(np.abs((true_values - predictions) / true_values)) * 100

print(f'RMSE: {rmse}')
print(f'MAE: {mae}')
print(f'MAPE: {mape:.2f}%')

RMSE: 0.7509319468696994
MAE: 0.5098981943527374
MAPE: inf%

	Fecha	Gasolina regular	Gasolina superior	Diesel	Gas licuado de petróleo
0	2000-01-01	202645.20	308156.82	634667.06	194410.476190
1	2000-02-01	205530.96	307766.31	642380.66	174710.552381
2	2000-03-01	229499.56	331910.29	699807.25	189234.066667
3	2000-04-01	210680.40	315648.08	586803.98	174330.607143
4	2000-05-01	208164.34	319667.97	656948.2	191745.147619

	Data Type	Unique Values%	Minimum Value	Maximum Value	DQ Issue
Fecha	object	100			Possible ID column: drop before modeling step.
Gasolina regular	float64	NA	160741.900000	894533.140000	No issue
Gasolina superior	float64	NA	300242.781667	768102.830000	Column has a high correlation with ['Gasolina regular']. Consider dropping one of them.
Diesel	float64	NA	507662.650000	1463008.430000	Column has a high correlation with ['Gasolina regular', 'Gasolina superior']. Consider dropping one of them.
Gas licuado de petróleo	float64	NA	167818.137897	588892.160000	Column has a high correlation with ['Gasolina regular', 'Gasolina superior', 'Diesel']. Consider dropping one of them.

	Fecha	Gasolina regular	Gasolina superior	Diesel	Gas licuado de petróleo
0	2001-01-01	177776.50	373963.96	566101.99	194065.738095
1	2001-02-01	123115.99	243091.07	489525.8	170703.380952
2	2001-03-01	161726.42	312084.38	575559.68	161837.371429
3	2001-04-01	127338.74	285054.89	437745.42	163048.642857
4	2001-05-01	168730.19	300913.67	552609.13	171518.861905

	Data Type	Unique Values%	Minimum Value	Maximum Value	DQ Issue
Fecha	object	100			Possible ID column: drop before modeling step.
Gasolina regular	float64	NA	81015.300000	987872.050000	No issue
Gasolina superior	float64	NA	170292.500000	1227173.530000	Column has 2 outliers greater than upper bound (939346.60) or lower than lower bound(23410.12). Cap them or remove them.
Diesel	float64	NA	229764.740000	1595698.560000	No issue
Gas licuado de petróleo	float64	NA	100561.466667	1077122.750000	Column has 1 outliers greater than upper bound (1076259.26) or lower than lower bound(-285424.28). Cap them or remove them.

	FECHA	Regular GTQ/GALON	Superior GTQ/GALON	Diesel GTQ/GALON	Glp Cilindro 25Lbs. GTQ/LB
0	2021-01-01	21.11	21.91	17.61	3.96
1	2021-01-02	21.11	21.91	17.61	3.96
2	2021-01-03	21.11	21.91	17.61	3.96
3	2021-01-04	21.11	21.91	17.61	3.96
4	2021-01-05	21.11	21.91	17.61	3.96

Universidad del Valle de Guatemala¶

Facultad de Ingeniería¶

Departamento de Computación¶

Laboratorio 2: Series de Tiempo¶

✍️ Discusión / Cálculos - [Series de Tiempo] ✍️

📚Dataset: CONSUMO-2024-05.xlsx¶

📚Dataset: IMPORTACION-VOLUMEN-2024-05.xlsx¶

📚Dataset: Precios-Promedio-Nacionales-Diarios-2024-3.xlsx¶

📚Datasets: Seleccion de series de tiempo.¶

📝 Datos iniciales sobre las series.¶

👀 Visualizacion de las series.¶

✍️ Transformaciones¶

Predicciones ultimos 3 años¶

Prediccion para 2024¶

Comportamiendo durante pandemia¶

	Fecha	Gasolina regular	Gasolina superior	Diesel	Gas licuado de petróleo
0	2000-01-01	202645.200000	308156.820000	6.346671e+05	194410.476190
1	2000-02-01	205530.960000	307766.310000	6.423807e+05	174710.552381
2	2000-03-01	229499.560000	331910.290000	6.998072e+05	189234.066667
3	2000-04-01	210680.400000	315648.080000	5.868040e+05	174330.607143
4	2000-05-01	208164.340000	319667.970000	6.569482e+05	191745.147619
5	2000-06-01	195088.740000	300347.700000	5.709199e+05	196321.933333
6	2000-07-01	204556.770000	308305.930000	5.764360e+05	192344.502381
7	2000-08-01	218388.510000	323011.860000	5.937708e+05	203727.080952
8	2000-09-01	213906.960000	326455.720000	5.901454e+05	194565.097619
9	2000-10-01	213606.650000	323843.630000	6.411134e+05	210261.826190
10	2000-11-01	209227.480000	320788.080000	6.570863e+05	205027.952619
11	2000-12-01	230020.700000	366987.560000	6.732823e+05	200247.555000
12	2001-01-01	164547.351667	300242.781667	5.693288e+05	179405.990278
13	2001-02-01	174838.811667	301483.401667	5.977498e+05	167818.137897
14	2001-03-01	201992.651667	355572.831667	7.360860e+05	186397.325992
15	2001-04-01	198683.391667	355826.761667	6.062577e+05	175820.345040
16	2001-05-01	182504.091667	336040.041667	6.640827e+05	178303.330754
17	2001-06-01	201379.481667	308804.701667	5.593330e+05	177826.928373
18	2001-07-01	214980.241667	350501.971667	5.894527e+05	191041.990278
19	2001-08-01	204646.771667	348414.651667	5.685464e+05	190295.990278
20	2001-09-01	188292.351667	324393.961667	5.331084e+05	184039.990278
21	2001-10-01	225240.951667	366261.631667	6.033712e+05	206176.990278
22	2001-11-01	210324.951667	351890.631667	6.261562e+05	201335.990278
23	2001-12-01	223209.951667	403561.631667	6.641902e+05	209792.990278
24	2002-01-01	200309.851667	368935.525833	7.266726e+05	196068.608730
25	2002-02-01	183119.341667	348917.785833	6.943448e+05	180209.801587
26	2002-03-01	212711.711667	385044.965833	6.563793e+05	186680.965873
27	2002-04-01	204765.621667	379741.975833	6.836462e+05	196167.161111
28	2002-05-01	201083.511667	375054.635833	7.071655e+05	201612.356349
29	2002-06-01	187629.421667	371138.585833	6.678207e+05	193024.399206
30	2002-07-01	201692.641667	399944.645833	6.567616e+05	204196.244444
31	2002-08-01	205240.771667	392355.615833	6.570997e+05	213625.432540
32	2002-09-01	177614.801667	347191.435833	6.536646e+05	209849.758730
33	2002-10-01	203097.241667	381595.195833	6.530533e+05	212932.327778
34	2002-11-01	207629.811667	384334.215833	6.714013e+05	210000.546825
35	2002-12-01	199619.271667	400645.415833	6.761924e+05	223830.396825
36	2003-01-01	193713.620000	376516.580000	7.698433e+05	213268.978571
37	2003-02-01	184354.610000	355760.810000	7.097836e+05	206815.011905
38	2003-03-01	201443.370000	361880.870000	7.864845e+05	216427.950000
39	2003-04-01	219004.312500	377395.170000	7.096264e+05	204403.511905
40	2003-05-01	200407.180000	390624.270000	7.559821e+05	216452.752143
41	2003-06-01	168327.720000	346941.840000	6.415401e+05	212069.040476
42	2003-07-01	174317.570000	377178.840000	6.317041e+05	231172.014286
43	2003-08-01	171665.050000	355682.720000	6.134286e+05	222312.707143
44	2003-09-01	174757.950000	348659.020000	5.829895e+05	225703.050000
45	2003-10-01	209314.450000	392041.780000	7.227636e+05	239665.042857
46	2003-11-01	168510.000000	330640.230000	6.398284e+05	229612.354762
47	2003-12-01	196081.990000	391327.660000	6.528724e+05	247544.638095
48	2004-01-01	203975.570000	381547.720000	6.569874e+05	235284.965714
49	2004-02-01	180290.240000	339576.460000	6.595404e+05	213288.683333
50	2004-03-01	206455.620000	379687.460000	7.462368e+05	233426.354762
51	2004-04-01	182379.920000	357595.810000	6.337661e+05	217530.876190
52	2004-05-01	160741.900000	322596.730000	5.563697e+05	225959.050238
53	2004-06-01	164782.500000	318811.500000	5.603935e+05	225673.464048
54	2004-07-01	182642.410000	346413.660000	5.830985e+05	243064.072857
55	2004-08-01	207432.800000	390304.280000	6.996108e+05	236324.768095
56	2004-09-01	194240.550000	364689.840000	6.126396e+05	225204.180238
57	2004-10-01	200323.530000	380441.550000	6.190848e+05	229626.058333
58	2004-11-01	214509.650000	378498.600000	7.052982e+05	225593.823095
59	2004-12-01	236733.030000	429109.100000	7.618643e+05	241915.005238
60	2005-01-01	209901.600000	321382.140000	7.246924e+05	227154.969762
61	2005-02-01	198775.610000	375768.830000	7.171168e+05	211814.375952
62	2005-03-01	231358.580000	410241.928000	8.021396e+05	224387.028333
63	2005-04-01	225644.890000	389737.160000	8.039969e+05	218555.206429
64	2005-05-01	187048.260000	343762.520000	7.153604e+05	230568.152381
65	2005-06-01	202161.790000	351906.820000	6.817434e+05	223151.259524
66	2005-07-01	213821.770000	389172.650000	6.786145e+05	236140.078571
67	2005-08-01	212696.380000	396871.120000	6.936811e+05	242734.740476
68	2005-09-01	196776.870000	378887.620000	6.662584e+05	223283.869048
69	2005-10-01	192270.240000	328052.560000	5.898302e+05	213139.580952
70	2005-11-01	185251.120000	360869.140000	7.012555e+05	222988.526190
71	2005-12-01	241035.130000	448688.520000	7.750272e+05	225410.429048
72	2006-01-01	211152.720000	382078.880000	7.509464e+05	217867.309524
73	2006-02-01	200551.400000	367944.250000	7.345308e+05	212328.607143
74	2006-03-01	233082.860000	427567.580000	8.750473e+05	232405.357143
75	2006-04-01	223479.450000	392050.290000	7.111852e+05	207612.269048
76	2006-05-01	222710.900000	379832.550000	7.342892e+05	232489.833333
77	2006-06-01	196511.220000	364359.350000	6.724289e+05	236563.828571
78	2006-07-01	210255.700000	392252.910000	6.844387e+05	239767.854762
79	2006-08-01	211202.140000	394585.280000	7.072197e+05	245672.016667
80	2006-09-01	204369.990000	382724.970000	6.507914e+05	256040.102381
81	2006-10-01	203061.390000	400793.810000	6.835431e+05	238514.633333
82	2006-11-01	205541.910000	401922.570000	7.391820e+05	228417.438095
83	2006-12-01	233936.830000	454373.130000	7.763303e+05	236333.111905
84	2007-01-01	216560.790000	418412.860000	8.103727e+05	217344.655952
85	2007-02-01	204991.910000	401425.140000	7.993946e+05	211149.360714
86	2007-03-01	239019.910000	454731.090000	9.566802e+05	264115.983333
87	2007-04-01	217536.230000	403732.560000	7.589886e+05	224818.876190
88	2007-05-01	224400.730000	426414.710000	8.117037e+05	259749.480952
89	2007-06-01	217473.910000	390991.360000	7.208968e+05	244505.283333
90	2007-07-01	219874.230000	416497.410000	7.215506e+05	261555.621429
91	2007-08-01	236565.570000	411088.530000	7.366209e+05	267634.121429
92	2007-09-01	207241.840000	390568.990000	6.695879e+05	254081.758571
93	2007-10-01	224750.770000	419001.810000	7.387029e+05	264936.659524
94	2007-11-01	218116.790000	417490.770000	7.795471e+05	250108.611905
95	2007-12-01	252394.114286	438542.793333	7.688869e+05	253659.528571
96	2008-01-01	213645.390000	403266.740000	7.976689e+05	252508.333333
97	2008-02-01	215873.670000	407735.550000	7.940002e+05	241978.083333
98	2008-03-01	233761.070000	424157.280000	7.760268e+05	247679.038095
99	2008-04-01	233687.280000	399193.530000	8.077258e+05	243178.030952
100	2008-05-01	236635.850000	390882.630000	6.823262e+05	240798.459524
101	2008-06-01	200656.570000	337464.320000	5.076627e+05	242422.571429
102	2008-07-01	228640.210000	374847.050000	5.443993e+05	243531.971429
103	2008-08-01	227499.000000	378363.760000	5.561744e+05	237062.519048
104	2008-09-01	218214.640000	367962.730000	5.578026e+05	225262.458571
105	2008-10-01	238955.440000	386430.400000	6.178631e+05	263100.502381
106	2008-11-01	238626.160000	375438.690000	6.881041e+05	230092.621429
107	2008-12-01	298245.470000	463343.730000	7.789538e+05	245852.645238
108	2009-01-01	273494.160000	424264.500000	8.005265e+05	217372.416667
109	2009-02-01	254667.700000	424438.710000	7.950505e+05	212038.241928
110	2009-03-01	290343.280000	434248.690000	8.659816e+05	235475.316667
111	2009-04-01	290887.080000	454265.400000	8.221097e+05	209739.173810
112	2009-05-01	276970.670000	432401.490000	7.706873e+05	221884.466190
113	2009-06-01	252551.990000	393140.380000	7.038155e+05	221329.626190
114	2009-07-01	283142.050000	435256.090000	7.408681e+05	241510.204762
115	2009-08-01	267830.840000	409182.960000	6.923767e+05	232174.109524
116	2009-09-01	248994.420000	420238.710000	6.874351e+05	225886.426190
117	2009-10-01	269008.520000	421753.290000	7.757396e+05	235899.133333
118	2009-11-01	212149.905000	391754.810000	7.386786e+05	232172.833333
119	2009-12-01	296805.290000	449842.290000	8.916704e+05	215923.378571
120	2010-01-01	243341.490000	389068.410000	7.777413e+05	228078.623810
121	2010-02-01	248207.930000	391195.690000	8.076921e+05	214409.685714
122	2010-03-01	320985.410000	465664.590000	1.017058e+06	242081.585714
123	2010-04-01	254872.610000	387063.180000	7.627700e+05	216150.666667
124	2010-05-01	238903.310000	381811.180000	7.216682e+05	215523.538095
125	2010-06-01	251896.390000	385981.100000	6.832365e+05	226415.073810
126	2010-07-01	278700.490000	422481.800000	7.215846e+05	243063.350000
127	2010-08-01	268917.790000	410176.390000	6.957963e+05	245908.416667
128	2010-09-01	266975.250000	396429.750000	6.875539e+05	233404.895238
129	2010-10-01	275031.210000	418305.070000	7.454155e+05	236852.345238
130	2010-11-01	273452.940000	409652.730000	7.859174e+05	255841.845238
131	2010-12-01	320807.170000	466766.780000	8.437620e+05	251825.204762
132	2011-01-01	285089.040000	411737.330000	8.506968e+05	244328.914286
133	2011-02-01	269301.380000	391131.100000	8.199405e+05	228478.995238
134	2011-03-01	281453.750000	393160.910000	9.004859e+05	255792.916667
135	2011-04-01	285598.370000	386749.270000	7.975272e+05	237582.676190
136	2011-05-01	253277.880000	371876.750000	8.038495e+05	247975.276190
137	2011-06-01	290137.490000	380800.430000	7.296768e+05	247082.276190
138	2011-07-01	279283.360000	389294.640000	7.182551e+05	259033.888095
139	2011-08-01	256380.270000	408494.510000	6.997361e+05	259688.742857
140	2011-09-01	256728.250000	380453.230000	6.603849e+05	251882.535714
141	2011-10-01	242071.010000	381309.500000	6.722149e+05	262255.138095
142	2011-11-01	231413.830000	381871.840000	7.945072e+05	252218.214286
143	2011-12-01	291678.030000	449904.100000	8.623188e+05	263892.433333
144	2012-01-01	261933.680000	393781.940000	8.149567e+05	242524.898571
145	2012-02-01	253733.220000	402633.550000	8.480672e+05	243810.952381
146	2012-03-01	290286.650000	429534.500000	9.250653e+05	270906.083810
147	2012-04-01	262615.280000	389264.020000	7.922017e+05	249493.658571
148	2012-05-01	235374.040000	405877.410000	7.715425e+05	261657.406667
149	2012-06-01	261012.580000	409111.240000	7.124649e+05	255333.426190
150	2012-07-01	233061.350000	420370.600000	6.991300e+05	271101.936190
151	2012-08-01	259165.940000	433828.020000	7.167204e+05	280184.268571
152	2012-09-01	228205.770000	378478.800000	6.321204e+05	268875.095238
153	2012-10-01	261347.220000	414523.220000	7.284149e+05	297829.174048
154	2012-11-01	272657.820000	394751.470000	8.125234e+05	303422.012381
155	2012-12-01	310407.040000	442191.430000	8.482629e+05	281757.561905
156	2013-01-01	287985.590000	408557.450000	8.637694e+05	291843.997619
157	2013-02-01	259659.660000	361215.690000	8.088540e+05	253221.892857
158	2013-03-01	313075.450000	413744.860000	8.983522e+05	286869.621429
159	2013-04-01	330070.650000	409238.300000	9.661039e+05	287216.303333
160	2013-05-01	313131.360000	403354.790000	8.276012e+05	301770.525476
161	2013-06-01	287596.640000	402012.070000	7.751871e+05	275921.369048
162	2013-07-01	314185.610000	425154.220000	7.325445e+05	292790.104762
163	2013-08-01	288400.670000	416968.150000	7.731479e+05	282844.319048
164	2013-09-01	249357.420000	379479.780000	6.721252e+05	278403.420238
165	2013-10-01	314518.720000	430179.690000	8.000491e+05	285195.011905
166	2013-11-01	306638.450000	404692.460000	8.214575e+05	253022.225476
167	2013-12-01	339969.850000	448135.350000	8.603519e+05	282335.469524
168	2014-01-01	312139.770000	412617.930000	9.172515e+05	288260.804286
169	2014-02-01	312142.650000	385430.580000	8.903623e+05	304312.128571
170	2014-03-01	335869.630000	434863.960000	1.004937e+06	284201.430952
171	2014-04-01	349155.300000	437470.070000	9.720394e+05	250902.831190
172	2014-05-01	332737.370000	427487.200000	8.772633e+05	305911.840476
173	2014-06-01	303016.820000	387299.100000	7.335087e+05	285640.435714
174	2014-07-01	344659.640000	439137.130000	8.269188e+05	289827.598571
175	2014-08-01	326356.060000	442953.100000	7.936940e+05	272617.597143
176	2014-09-01	371327.130000	411466.280000	7.284988e+05	271079.594762
177	2014-10-01	343812.670000	459922.480000	8.362781e+05	337006.144524
178	2014-11-01	343918.560000	436467.900000	8.857137e+05	303666.704286
179	2014-12-01	464223.150000	538899.260000	1.021378e+06	311419.960476
180	2015-01-01	419102.720000	503135.700000	1.014706e+06	317220.912857
181	2015-02-01	398167.690000	454190.880000	9.801498e+05	315364.363333
182	2015-03-01	457246.140000	521879.260000	1.142551e+06	338142.317143
183	2015-04-01	413451.110000	486779.410000	1.011168e+06	320920.883810
184	2015-05-01	415570.950000	483594.830000	9.443167e+05	328177.803810
185	2015-06-01	414466.180000	480892.110000	8.800165e+05	331140.045714
186	2015-07-01	440729.530000	515322.660000	9.025864e+05	363251.960000
187	2015-08-01	439588.900000	513413.000000	8.877968e+05	333215.607143
188	2015-09-01	422826.585476	501658.043333	7.346542e+05	364351.758095
189	2015-10-01	448971.880000	532741.140000	8.342859e+05	364469.545714
190	2015-11-01	423841.820000	516486.950000	9.254310e+05	353643.078571
191	2015-12-01	499839.170000	600161.720000	1.047479e+06	355233.649048
192	2016-01-01	433180.340000	545737.530000	1.084044e+06	346512.219048
193	2016-02-01	450916.350000	540459.190000	1.055673e+06	368867.548095
194	2016-03-01	486255.940000	575720.920000	1.107297e+06	372223.424048
195	2016-04-01	467472.200000	545605.470000	1.149175e+06	357084.223333
196	2016-05-01	468710.380000	551000.790000	1.019984e+06	377629.016905
197	2016-06-01	450758.180000	537583.250000	8.885381e+05	355848.993333
198	2016-07-01	465270.480000	562778.840000	8.837160e+05	350429.766667
199	2016-08-01	485995.830000	585748.500000	8.662700e+05	363859.513095
200	2016-09-01	456946.750000	548491.650000	8.778006e+05	356426.233571
201	2016-10-01	476277.640000	573754.730000	9.164107e+05	373503.775714
202	2016-11-01	463726.210000	547323.530000	1.019672e+06	372904.634286
203	2016-12-01	524242.310000	615251.510000	1.113827e+06	372690.455714
204	2017-01-01	477291.930000	546212.160000	1.079339e+06	358706.818571
205	2017-02-01	453665.320000	526888.940000	1.052536e+06	357497.970229
206	2017-03-01	523943.600000	592249.340000	1.216423e+06	351791.925033
207	2017-04-01	500063.030000	530345.960000	1.032813e+06	360957.704524
208	2017-05-01	516884.370000	588861.620000	1.034806e+06	377492.160714
209	2017-06-01	478491.950000	549378.050000	8.975119e+05	378328.463810
210	2017-07-01	521730.410000	607702.700000	9.388876e+05	369948.637476
211	2017-08-01	514086.570000	604650.340000	9.501881e+05	385834.284762
212	2017-09-01	492618.550000	560892.670000	8.943636e+05	447373.592143
213	2017-10-01	512762.320000	583658.830000	9.359862e+05	393901.921429
214	2017-11-01	510631.290000	563345.610000	1.038598e+06	409332.858095
215	2017-12-01	574097.700000	635423.760000	1.117711e+06	404574.361905
216	2018-01-01	528763.300000	581802.980000	1.105629e+06	421848.920476
217	2018-02-01	502041.670000	545210.000000	1.072072e+06	379239.067143
218	2018-03-01	571052.130000	618402.180000	1.194136e+06	415125.896429
219	2018-04-01	526979.670000	565665.720000	1.108453e+06	370434.605238
220	2018-05-01	544395.170000	570129.660000	1.009750e+06	403094.744048
221	2018-06-01	518911.440000	554431.500000	9.300650e+05	390070.171667
222	2018-07-01	541811.120000	589775.970000	9.503435e+05	381128.075238
223	2018-08-01	570224.107000	593659.630000	9.918181e+05	407124.746190
224	2018-09-01	531285.291000	551547.830000	8.823035e+05	385717.790714
225	2018-10-01	573751.472000	579474.430000	9.690950e+05	408674.336524
226	2018-11-01	563417.572000	573687.350000	1.061942e+06	408049.579000
227	2018-12-01	632670.174000	654281.250000	1.106130e+06	421872.087524
228	2019-01-01	596682.750000	601714.900000	1.152950e+06	421555.410000
229	2019-02-01	559109.220000	565425.020000	1.103358e+06	402371.330000
230	2019-03-01	645266.770000	610720.580000	1.228025e+06	428149.450000
231	2019-04-01	657570.180000	598956.300000	1.160125e+06	414018.680000
232	2019-05-01	666748.630000	611592.320000	1.134666e+06	426587.330000
233	2019-06-01	641136.560000	595744.380000	1.012030e+06	399031.610000
234	2019-07-01	650150.780000	615559.330000	1.030650e+06	419958.020000
235	2019-08-01	668783.390000	632415.700000	1.070198e+06	419493.820000
236	2019-09-01	616079.300000	587861.840000	9.733584e+05	396860.640000
237	2019-10-01	669008.770000	643044.330000	1.043630e+06	433917.150000
238	2019-11-01	643070.350000	620572.210000	1.122735e+06	430725.890000
239	2019-12-01	706020.770000	682468.140000	1.175725e+06	428697.640000
240	2020-01-01	640011.730000	622323.790000	1.184419e+06	421644.720000
241	2020-02-01	652028.000000	633100.050000	1.233250e+06	426533.150000
242	2020-03-01	573586.730000	509752.950000	1.100814e+06	420637.140000
243	2020-04-01	458420.850000	391773.230000	8.675923e+05	345933.610000
244	2020-05-01	478771.830000	407482.520000	8.287439e+05	362987.370000
245	2020-06-01	516933.600000	388952.810000	7.823280e+05	402541.020000
246	2020-07-01	567052.500000	462380.810000	8.703549e+05	423328.220000
247	2020-08-01	647375.580000	574379.330000	8.852007e+05	483695.900000
248	2020-09-01	661665.580000	610445.100000	9.590133e+05	439581.360000
249	2020-10-01	680362.070000	648582.820000	1.064005e+06	459312.450000
250	2020-11-01	652630.160000	639856.620000	1.072873e+06	421504.590000
251	2020-12-01	768193.030000	760695.450000	1.276291e+06	481068.860000
252	2021-01-01	685942.970000	655512.660000	1.220638e+06	485759.130000
253	2021-02-01	669312.880000	621712.090000	1.272609e+06	447847.050000
254	2021-03-01	773453.520000	692240.490000	1.417380e+06	513966.390000
255	2021-04-01	688389.030000	610310.750000	1.268491e+06	466275.270000
256	2021-05-01	707336.380000	643076.840000	1.188223e+06	475018.360000
257	2021-06-01	692598.710000	637332.810000	1.128077e+06	475462.790000
258	2021-07-01	740193.130000	665472.940000	1.197398e+06	500478.600000
259	2021-08-01	668221.540000	636824.610000	1.104626e+06	492629.810000
260	2021-09-01	670418.800000	605257.760000	1.102655e+06	493058.470000
261	2021-10-01	718676.320000	675821.540000	1.166983e+06	497279.700000
262	2021-11-01	708246.270000	641916.400000	1.223817e+06	480432.290000
263	2021-12-01	821882.150000	768102.830000	1.380065e+06	531775.930000
264	2022-01-01	683031.840000	621512.780000	1.281083e+06	513015.930000
265	2022-02-01	661521.580000	592106.140000	1.267195e+06	519921.040000
266	2022-03-01	701819.650000	598038.470000	1.335368e+06	483657.430000
267	2022-04-01	812059.060000	569199.430000	1.288233e+06	493178.400000
268	2022-05-01	791824.980000	483991.630000	1.109577e+06	515616.020000
269	2022-06-01	781009.470000	507260.420000	1.102689e+06	513914.270000
270	2022-07-01	788841.460000	554496.900000	1.125984e+06	513619.250000
271	2022-08-01	784468.320000	581844.420000	1.107349e+06	541273.940000
272	2022-09-01	760396.150000	570632.200000	1.202883e+06	479529.680000
273	2022-10-01	768784.360000	573943.480000	9.792433e+05	545823.450000
274	2022-11-01	771173.120000	572163.230000	1.165294e+06	528393.770000
275	2022-12-01	865851.440000	635953.460000	1.241282e+06	556737.220000
276	2023-01-01	804524.730000	599501.290000	1.285933e+06	574148.000000
277	2023-02-01	737345.640000	544144.000000	1.226885e+06	497273.190000
278	2023-03-01	875010.340000	656941.640000	1.463008e+06	588892.160000
279	2023-04-01	798128.360000	586642.200000	1.247899e+06	513919.890000
280	2023-05-01	866826.790000	646221.200000	1.346554e+06	561767.170000
281	2023-06-01	782745.170000	616290.210000	1.159046e+06	532138.900000
282	2023-07-01	825219.050000	642098.160000	1.178454e+06	521833.560000
283	2023-08-01	851754.020000	632333.400000	1.222453e+06	543825.640000
284	2023-09-01	799852.730000	597670.260000	1.103622e+06	517699.480000
285	2023-10-01	728741.160000	531542.580000	9.981134e+05	495261.140000
286	2023-11-01	807947.980000	632180.030000	1.248224e+06	530542.420000
287	2023-12-01	880233.120000	691389.620000	1.334692e+06	541263.180000
288	2024-01-01	830708.130000	658083.660000	1.359012e+06	548124.450000
289	2024-02-01	818740.160000	654059.600000	1.340174e+06	526897.850000
290	2024-03-01	870771.700000	671997.050000	1.393325e+06	523990.910000
291	2024-04-01	847353.150000	633520.570000	1.428143e+06	531880.190000
292	2024-05-01	894533.140000	692427.940000	1.401052e+06	536754.380000

	Fecha	Gasolina regular	Gasolina superior	Diesel	Gas licuado de petróleo
0	2001-01-01	177776.500000	373963.960	566101.990	1.940657e+05
1	2001-02-01	123115.990000	243091.070	489525.800	1.707034e+05
2	2001-03-01	161726.420000	312084.380	575559.680	1.618374e+05
3	2001-04-01	127338.740000	285054.890	437745.420	1.630486e+05
4	2001-05-01	168730.190000	300913.670	552609.130	1.715189e+05
5	2001-06-01	152899.090000	333217.190	497855.260	1.900044e+05
6	2001-07-01	136299.130000	195071.860	302350.020	2.060228e+05
7	2001-08-01	139365.070000	268153.260	464159.130	1.005615e+05
8	2001-09-01	233643.750000	308439.070	321952.940	1.868390e+05
9	2001-10-01	141550.220000	305102.280	438989.550	1.638642e+05
10	2001-11-01	165841.460000	256638.190	440245.350	1.947225e+05
11	2001-12-01	149217.670000	277145.050	479983.270	1.943290e+05
12	2002-01-01	178585.930000	271479.000	500041.320	1.684226e+05
13	2002-02-01	144447.250000	365657.750	686923.830	2.001414e+05
14	2002-03-01	104673.640000	246292.600	432538.460	1.934226e+05
15	2002-04-01	190339.000000	310256.300	424667.360	2.182575e+05
16	2002-05-01	194830.100000	363311.760	497781.660	1.592994e+05
17	2002-06-01	137050.800000	250123.190	496826.620	2.108746e+05
18	2002-07-01	222408.000000	428842.800	636727.840	1.997085e+05
19	2002-08-01	143795.000000	298280.330	346225.000	1.922137e+05
20	2002-09-01	156956.900000	331053.700	638933.220	1.906867e+05
21	2002-10-01	195426.800000	352055.500	703749.810	2.108712e+05
22	2002-11-01	153185.700000	291188.760	573289.500	2.275959e+05
23	2002-12-01	167596.710000	440677.810	678749.640	1.922720e+05
24	2003-01-01	228269.500000	425185.500	797344.380	2.375323e+05
25	2003-02-01	214490.700000	443849.850	765912.660	1.698190e+05
26	2003-03-01	161257.480000	363352.660	693942.310	2.210309e+05
27	2003-04-01	189824.893000	335444.690	873103.096	1.921701e+05
28	2003-05-01	269981.860000	459025.690	625190.080	1.850167e+05
29	2003-06-01	182254.230000	372486.530	671882.830	2.318050e+05
30	2003-07-01	133668.200000	400922.970	822014.550	2.165049e+05
31	2003-08-01	169903.860000	292365.040	494087.130	2.099524e+05
32	2003-09-01	177217.530000	398192.160	614913.680	2.314992e+05
33	2003-10-01	181668.026000	260219.920	433084.990	2.163028e+05
34	2003-11-01	205624.080000	393951.960	627189.780	2.325270e+05
35	2003-12-01	161477.730000	296780.150	745524.880	2.144899e+05
36	2004-01-01	173427.230000	368235.140	762385.450	2.512715e+05
37	2004-02-01	147939.230000	281891.970	460947.500	1.977303e+05
38	2004-03-01	275539.380000	486880.820	772128.260	2.533661e+05
39	2004-04-01	245156.340000	415426.000	809194.950	2.066719e+05
40	2004-05-01	178387.800000	350052.950	634209.640	2.023682e+05
41	2004-06-01	133341.000000	311238.010	461381.790	2.318503e+05
42	2004-07-01	152075.750000	249710.687	467154.310	2.204493e+05
43	2004-08-01	208592.850000	434578.120	669462.530	2.158811e+05
44	2004-09-01	218499.240000	420550.290	714039.850	2.520583e+05
45	2004-10-01	201907.670000	366886.300	655715.050	2.087155e+05
46	2004-11-01	215838.320000	434710.940	726697.880	1.956455e+05
47	2004-12-01	227094.710000	433149.960	781266.610	2.786885e+05
48	2005-01-01	202374.330000	257867.990	663058.790	2.264525e+05
49	2005-02-01	178104.720000	306660.390	683664.390	1.792128e+05
50	2005-03-01	217667.690000	431907.810	778645.070	2.711630e+05
51	2005-04-01	234492.030000	534151.810	708164.740	1.876000e+05
52	2005-05-01	192734.400000	378616.300	875087.620	2.436253e+05
53	2005-06-01	243343.680000	537398.430	1041016.720	2.560014e+05
54	2005-07-01	175946.300000	363410.900	770731.740	3.510896e+05
55	2005-08-01	173810.380000	566618.550	815435.200	1.150986e+05
56	2005-09-01	81015.300000	193484.500	229764.740	4.619572e+05
57	2005-10-01	145325.650000	528877.090	671848.940	4.567143e+05
58	2005-11-01	266018.370000	348687.810	635553.460	1.278198e+05
59	2005-12-01	107374.000000	360807.000	1212610.530	4.564411e+05
60	2006-01-01	348503.850000	711860.770	708730.840	4.619158e+05
61	2006-02-01	145048.200000	262170.900	767710.630	1.393182e+05
62	2006-03-01	285783.250000	408065.340	744721.940	3.852874e+05
63	2006-04-01	238824.610000	421300.820	935306.580	3.177561e+05
64	2006-05-01	134212.300000	312024.700	822556.450	1.345260e+05
65	2006-06-01	211562.740000	394014.850	361207.590	3.475493e+05
66	2006-07-01	180942.180000	375951.160	971716.720	1.400744e+05
67	2006-08-01	147245.490000	313326.860	603747.330	4.340132e+05
68	2006-09-01	366193.770000	525623.150	473431.310	4.576832e+05
69	2006-10-01	153997.350000	285505.000	1220719.640	1.309387e+05
70	2006-11-01	171327.010000	428590.500	431874.860	4.920634e+05
71	2006-12-01	234319.300000	395132.090	890336.680	1.129128e+05
72	2007-01-01	289550.590000	523730.310	818060.570	3.083485e+05
73	2007-02-01	299077.040000	401855.410	822325.650	2.950828e+05
74	2007-03-01	326317.750000	541817.850	569451.850	3.993872e+05
75	2007-04-01	94844.900000	203441.330	850138.110	3.917908e+05
76	2007-05-01	238404.830000	545587.630	793887.040	1.321066e+05
77	2007-06-01	170064.980000	441537.390	855757.140	5.532520e+05
78	2007-07-01	227660.470000	451180.820	1118432.250	1.531678e+05
79	2007-08-01	351306.270000	501840.270	824047.470	3.096471e+05
80	2007-09-01	181015.830000	336120.810	497008.840	1.834849e+05
81	2007-10-01	312120.180000	376377.750	885924.860	4.861949e+05
82	2007-11-01	289282.550000	512550.510	665114.830	1.486274e+05
83	2007-12-01	222108.350000	542916.970	812776.590	3.710984e+05
84	2008-01-01	107885.650000	204788.300	613819.330	5.226705e+05
85	2008-02-01	234830.100000	545139.830	500922.230	1.269412e+05
86	2008-03-01	241800.330000	405713.480	1095743.350	3.212948e+05
87	2008-04-01	271554.270000	570256.980	682042.070	2.965320e+05
88	2008-05-01	242628.160000	424954.880	710464.760	5.065898e+05
89	2008-06-01	177838.700000	273398.730	817804.840	1.356305e+05
90	2008-07-01	232129.200000	384549.700	541486.150	3.651176e+05
91	2008-08-01	138376.720000	241881.470	713650.520	1.249092e+05
92	2008-09-01	290621.260000	423244.170	544521.540	2.903307e+05
93	2008-10-01	358036.440000	329762.300	344273.020	5.158331e+05
94	2008-11-01	170365.320000	386365.050	809439.420	3.047807e+05
95	2008-12-01	238398.920000	458949.420	524038.420	2.658542e+05
96	2009-01-01	202484.534762	382300.560	941535.710	4.408072e+05
97	2009-02-01	588346.360000	522820.810	914059.400	3.088982e+05
98	2009-03-01	298459.250000	626630.990	893473.800	1.163574e+05
99	2009-04-01	182218.960000	348267.660	816840.020	3.827966e+05
100	2009-05-01	377337.480000	341700.480	621339.930	1.080326e+05
101	2009-06-01	363676.290000	589337.790	735260.510	4.399545e+05
102	2009-07-01	363911.510000	337172.650	850806.890	4.518928e+05
103	2009-08-01	210495.680000	476525.360	842742.170	1.511795e+05
104	2009-09-01	161783.680000	321728.950	611966.220	2.655124e+05
105	2009-10-01	375067.230000	639524.610	869366.310	2.942996e+05
106	2009-11-01	201213.100000	321859.440	894309.880	5.434985e+05
107	2009-12-01	291525.480000	435416.180	1144960.660	1.212566e+05
108	2010-01-01	173918.350000	318949.720	905803.650	4.370423e+05
109	2010-02-01	336005.720000	492452.550	447271.920	4.108464e+05
110	2010-03-01	157798.770000	303762.940	947702.880	1.484954e+05
111	2010-04-01	433108.230000	562996.070	613011.210	4.100011e+05
112	2010-05-01	279926.950000	429729.380	1086036.160	2.305846e+05
113	2010-06-01	281834.460000	464310.800	587828.260	3.446831e+05
114	2010-07-01	241994.240000	456588.390	712889.930	4.619780e+05
115	2010-08-01	282509.240000	475714.120	637252.290	2.361643e+05
116	2010-09-01	208796.020000	321134.560	741721.420	1.443818e+05
117	2010-10-01	392100.620000	493794.290	630692.560	3.284726e+05
118	2010-11-01	139547.730000	241062.760	565627.540	5.406719e+05
119	2010-12-01	430857.950000	652210.770	1135293.920	2.865017e+05
120	2011-01-01	253700.820000	391997.540	1033948.690	5.115181e+05
121	2011-02-01	343582.850000	437769.480	772775.580	3.138772e+05
122	2011-03-01	291087.160000	445920.270	707536.310	2.971903e+05
123	2011-04-01	326003.710000	357349.670	767445.360	4.263802e+05
124	2011-05-01	326566.610000	433038.130	918940.440	2.383741e+05
125	2011-06-01	230990.130000	354604.940	553883.420	3.451963e+05
126	2011-07-01	188581.400000	369694.760	734958.540	1.489885e+05
127	2011-08-01	201844.490000	270765.310	1014324.280	5.656279e+05
128	2011-09-01	260669.220000	335803.820	795554.710	3.823104e+05
129	2011-10-01	239711.910000	351555.660	612133.260	3.190910e+05
130	2011-11-01	237803.480000	450212.800	575406.480	3.843684e+05
131	2011-12-01	367820.540000	534925.360	732921.030	1.410525e+05
132	2012-01-01	318283.440000	434648.170	790123.230	4.817815e+05
133	2012-02-01	253583.960000	372893.490	887963.980	1.625613e+05
134	2012-03-01	255492.160000	354160.850	978693.290	5.214937e+05
135	2012-04-01	189948.790000	399701.690	697871.280	1.319584e+05
136	2012-05-01	241786.290000	380531.850	833370.090	5.105068e+05
137	2012-06-01	320566.690000	550379.330	767124.650	5.276723e+05
138	2012-07-01	142232.470000	271509.200	1021995.900	1.654242e+05
139	2012-08-01	263677.230000	361307.200	626706.230	3.878921e+05
140	2012-09-01	201347.560000	372989.290	619651.270	5.308978e+05
141	2012-10-01	374301.510000	600094.030	463054.770	1.622084e+05
142	2012-11-01	197398.800000	359818.540	538552.890	5.693459e+05
143	2012-12-01	287980.580000	419029.170	1324780.960	1.529268e+05
144	2013-01-01	396416.840000	433924.690	749266.680	5.753185e+05
145	2013-02-01	235502.580000	470483.980	709843.020	1.427728e+05
146	2013-03-01	439386.620000	429974.170	832627.570	5.690132e+05
147	2013-04-01	274680.650000	430259.180	860094.780	5.086495e+05
148	2013-05-01	281823.200000	532379.660	1202187.850	1.814616e+05
149	2013-06-01	293808.600000	284038.030	729711.880	5.368472e+05
150	2013-07-01	259025.830000	486075.060	693160.500	3.529090e+05
151	2013-08-01	221433.200000	347066.110	505133.440	3.593460e+05
152	2013-09-01	300794.990000	373303.310	803626.980	3.949859e+05
153	2013-10-01	281168.850000	372946.330	980327.980	4.158693e+05
154	2013-11-01	390941.150000	496029.720	903218.470	4.256692e+05
155	2013-12-01	210096.780000	358799.640	791698.890	4.524141e+05
156	2014-01-01	487880.790000	457365.460	1595698.560	3.718887e+05
157	2014-02-01	312423.860000	455726.060	688703.180	3.368452e+05
158	2014-03-01	472437.630000	582572.930	1147182.500	3.587558e+05
159	2014-04-01	207049.000000	270302.220	845051.330	4.667670e+05
160	2014-05-01	417767.240000	563319.790	1008148.070	5.547843e+05
161	2014-06-01	247114.990000	404603.550	694920.680	3.591356e+05
162	2014-07-01	454196.130000	558403.410	845875.860	4.175703e+05
163	2014-08-01	305285.530000	398563.480	760353.370	4.160683e+05
164	2014-09-01	378741.560000	547486.220	598145.890	4.089989e+05
165	2014-10-01	256340.490000	379572.070	762763.240	3.148689e+05
166	2014-11-01	372809.930000	533524.400	1033873.160	4.873437e+05
167	2014-12-01	491072.110000	583081.570	1004032.650	3.801860e+05
168	2015-01-01	371420.590000	471973.300	1324421.960	5.233523e+05
169	2015-02-01	548714.530000	664315.850	955286.020	4.212682e+05
170	2015-03-01	453536.190000	622883.100	1164885.970	4.971686e+05
171	2015-04-01	537064.590000	599558.550	1130870.790	5.196038e+05
172	2015-05-01	534049.280000	618319.210	954782.750	4.158903e+05
173	2015-06-01	394824.330000	525280.850	862071.770	5.263585e+05
174	2015-07-01	519374.150000	616269.010	1149630.430	4.119714e+05
175	2015-08-01	328552.070000	467373.130	919285.900	5.615638e+05
176	2015-09-01	485906.490000	576250.290	754068.770	4.802689e+05
177	2015-10-01	432666.750000	533620.640	1163425.950	4.232394e+05
178	2015-11-01	574547.760000	724273.970	904700.420	6.373165e+05
179	2015-12-01	319329.710000	498798.790	1113876.280	5.006843e+05
180	2016-01-01	546876.540000	729211.430	987507.780	7.489706e+05
181	2016-02-01	424074.810000	405559.450	1167227.960	2.873377e+05
182	2016-03-01	508542.180000	771024.110	1498844.950	5.044511e+05
183	2016-04-01	527083.640000	502728.880	1137213.100	5.874827e+05
184	2016-05-01	528347.810000	615900.310	1035544.080	4.169125e+05
185	2016-06-01	397477.400000	502082.450	800222.530	7.033192e+05
186	2016-07-01	526652.730000	618366.100	859222.620	4.558231e+05
187	2016-08-01	512202.570000	625218.900	701185.750	5.883105e+05
188	2016-09-01	567786.570000	677892.970	1274128.310	3.065434e+05
189	2016-10-01	455850.760000	546154.650	927923.500	7.173886e+05
190	2016-11-01	476784.160000	725548.160	957656.050	5.059760e+05
191	2016-12-01	473659.590000	526573.960	1303735.780	5.848072e+05
192	2017-01-01	527564.900000	748233.580	842993.490	5.673458e+05
193	2017-02-01	469140.060000	497707.410	844676.980	2.795272e+05
194	2017-03-01	608144.980000	734984.240	1351787.920	7.800486e+05
195	2017-04-01	681303.110000	632843.410	1406022.430	5.305781e+05
196	2017-05-01	386088.180000	442311.090	889926.860	6.047026e+05
197	2017-06-01	519389.660000	582374.340	902659.000	4.872442e+05
198	2017-07-01	433026.080000	769214.060	994338.300	5.104281e+05
199	2017-08-01	592103.310000	607368.720	846696.350	5.385821e+05
200	2017-09-01	316435.220000	401678.980	925397.870	3.502946e+05
201	2017-10-01	574073.610000	657241.760	740198.570	5.847096e+05
202	2017-11-01	434963.360000	504247.310	1135350.100	9.359865e+05
203	2017-12-01	679441.470000	803216.880	1305047.550	5.954214e+05
204	2018-01-01	571858.060000	535784.700	1153831.890	3.963630e+05
205	2018-02-01	509366.100000	629467.910	921684.120	5.910000e+05
206	2018-03-01	563899.060000	529507.150	889227.100	6.442454e+05
207	2018-04-01	510830.000000	617512.280	696962.770	1.885908e+05
208	2018-05-01	580825.110000	580329.350	1592580.340	6.362341e+05
209	2018-06-01	463620.610000	558257.590	777679.910	6.188113e+05
210	2018-07-01	543333.770000	784601.770	793683.410	5.843861e+05
211	2018-08-01	663834.040000	541557.340	1281364.780	4.086586e+05
212	2018-09-01	454442.870000	536897.090	896266.430	4.498468e+05
213	2018-10-01	726044.350000	663440.340	992682.040	6.002305e+05
214	2018-11-01	534084.490000	509382.860	899588.340	5.986262e+05
215	2018-12-01	535639.970000	631652.560	792198.660	6.148870e+05
216	2019-01-01	702923.370000	684504.840	1195728.200	4.140861e+05
217	2019-02-01	469437.280000	521555.380	1208476.160	4.605188e+05
218	2019-03-01	678145.380000	704276.530	1550052.840	3.844256e+05
219	2019-04-01	867761.340000	656495.950	1215209.050	6.706339e+05
220	2019-05-01	596798.220000	568128.410	1011872.870	6.234662e+05
221	2019-06-01	712731.800000	629788.430	1029100.900	6.127323e+05
222	2019-07-01	531577.640000	516273.850	1056569.110	6.674868e+05
223	2019-08-01	896841.310000	713859.980	1167556.890	5.864306e+05
224	2019-09-01	518043.680000	491194.710	717490.020	8.507782e+05
225	2019-10-01	712672.380000	691407.730	1160570.850	2.252070e+05
226	2019-11-01	791039.020000	658706.420	1122990.690	6.108681e+05
227	2019-12-01	731012.240000	797580.750	1098972.390	6.000039e+05
228	2020-01-01	629414.900000	573220.630	999181.200	7.156003e+05
229	2020-02-01	741509.070000	611314.130	1237018.370	4.513317e+05
230	2020-03-01	735491.290000	809640.180	1367335.140	6.432789e+05
231	2020-04-01	329134.180000	453153.090	834366.520	5.103499e+05
232	2020-05-01	415805.820000	170292.500	691066.440	6.163814e+05
233	2020-06-01	683322.720000	415672.190	1066148.050	4.676648e+05
234	2020-07-01	451717.750000	347541.590	779930.570	6.753375e+05
235	2020-08-01	547899.550000	414416.340	791258.190	6.343201e+05
236	2020-09-01	591799.830000	563742.500	1021360.830	3.155271e+05
237	2020-10-01	861840.540000	733692.010	891340.690	9.608405e+05
238	2020-11-01	696600.140000	570262.170	1152909.510	3.286031e+05
239	2020-12-01	780347.280000	777450.550	1050560.830	6.841601e+05
240	2021-01-01	626537.480000	642652.370	1185644.960	4.756195e+05
241	2021-02-01	715261.340000	726508.780	1076824.670	6.177832e+05
242	2021-03-01	772750.010000	850334.640	1345110.180	5.498153e+05
243	2021-04-01	634408.890000	1227173.530	1250171.580	6.644439e+05
244	2021-05-01	669085.780000	649783.140	1279017.240	6.785011e+05
245	2021-06-01	833544.640000	623695.200	1440106.470	4.666585e+05
246	2021-07-01	833732.310000	689717.290	1110409.530	6.652631e+05
247	2021-08-01	664522.740000	685977.450	1175960.640	7.460025e+05
248	2021-09-01	844430.030000	646233.310	896539.240	6.898295e+05
249	2021-10-01	522701.110000	589405.560	1542473.570	6.963469e+05
250	2021-11-01	692267.310000	623392.630	1029780.800	5.241865e+05
251	2021-12-01	785313.560000	640981.150	1294042.310	6.794151e+05
252	2022-01-01	848902.970000	947226.270	1438571.280	6.571857e+05
253	2022-02-01	723925.740000	673653.800	1217495.310	6.732183e+05
254	2022-03-01	772406.100000	592318.210	1246975.120	7.135534e+05
255	2022-04-01	906104.050000	493958.320	1284453.730	6.939644e+05
256	2022-05-01	762776.120000	663348.940	1306786.770	6.672722e+05
257	2022-06-01	641348.460000	556311.960	1032070.530	2.657738e+05
258	2022-07-01	987872.050000	611115.010	1116548.970	7.176527e+05
259	2022-08-01	657942.720000	378627.690	1299910.960	7.352537e+05
260	2022-09-01	869579.640000	551589.720	1077517.900	4.213032e+05
261	2022-10-01	718920.200000	529832.420	980527.080	7.178492e+05
262	2022-11-01	754135.860000	595870.420	1136760.550	6.912374e+05
263	2022-12-01	841323.980000	602458.300	1082107.640	6.714446e+05
264	2023-01-01	909391.130000	578792.140	1442099.080	4.977807e+05
265	2023-02-01	725101.200000	685183.060	1267967.390	6.529847e+05
266	2023-03-01	803262.670000	633849.050	1317519.910	7.119783e+05
267	2023-04-01	922032.390000	572201.360	1417182.730	6.476663e+05
268	2023-05-01	947633.290000	668478.730	1428099.620	7.133490e+05
269	2023-06-01	831466.440000	639685.800	1278824.180	2.858178e+05
270	2023-07-01	830098.860000	562498.090	1113086.840	1.077123e+06
271	2023-08-01	801621.750000	669240.210	1075379.760	2.749736e+05
272	2023-09-01	762592.300000	586145.300	1242719.420	5.917119e+05
273	2023-10-01	928439.180000	652149.450	834686.020	6.735812e+05
274	2023-11-01	839290.020000	682060.840	1348739.160	5.787974e+05
275	2023-12-01	763754.270000	571924.920	1509634.280	6.921820e+05
276	2024-01-01	914133.320000	712333.330	1409097.150	7.015708e+05
277	2024-02-01	740662.250000	650360.110	1236861.750	9.165417e+05
278	2024-03-01	838270.930000	620077.740	1477038.000	6.751575e+05
279	2024-04-01	886132.770000	687017.960	1294706.120	4.739407e+05
280	2024-05-01	939656.180000	696970.300	1470870.090	6.848645e+05